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Create Merged .csv File with Data from All Cities

Data has already been processed

Import Data & Data Info

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1066968 entries, 0 to 1066967
Data columns (total 37 columns):
 #   Column             Non-Null Count    Dtype  
---  ------             --------------    -----  
 0   date_time          1066968 non-null  object 
 1   sunrise            1066968 non-null  object 
 2   sunset             1066968 non-null  object 
 3   moonrise           1066968 non-null  object 
 4   moonset            1066968 non-null  object 
 5   moon_phase         1066968 non-null  object 
 6   moon_illumination  1066968 non-null  int64  
 7   time               1066968 non-null  int64  
 8   tempC              1066968 non-null  int64  
 9   tempF              1066968 non-null  int64  
 10  windspeedMiles     1066968 non-null  int64  
 11  windspeedKmph      1066968 non-null  int64  
 12  winddirDegree      1066968 non-null  int64  
 13  winddir16Point     1066968 non-null  object 
 14  weatherCode        1066968 non-null  int64  
 15  weatherIconUrl     1066968 non-null  object 
 16  weatherDesc        1066968 non-null  object 
 17  precipMM           1066968 non-null  float64
 18  precipInches       1066968 non-null  float64
 19  humidity           1066968 non-null  int64  
 20  visibility         1066968 non-null  int64  
 21  visibilityMiles    1066968 non-null  int64  
 22  pressure           1066968 non-null  int64  
 23  pressureInches     1066968 non-null  int64  
 24  cloudcover         1066968 non-null  int64  
 25  HeatIndexC         1066968 non-null  int64  
 26  HeatIndexF         1066968 non-null  int64  
 27  DewPointC          1066968 non-null  int64  
 28  DewPointF          1066968 non-null  int64  
 29  WindChillC         1066968 non-null  int64  
 30  WindChillF         1066968 non-null  int64  
 31  WindGustMiles      1066968 non-null  int64  
 32  WindGustKmph       1066968 non-null  int64  
 33  FeelsLikeC         1066968 non-null  int64  
 34  FeelsLikeF         1066968 non-null  int64  
 35  uvIndex            1066968 non-null  int64  
 36  city               1066968 non-null  object 
dtypes: float64(2), int64(25), object(10)
memory usage: 301.2+ MB

date_time            0
sunrise              0
sunset               0
moonrise             0
moonset              0
moon_phase           0
moon_illumination    0
time                 0
tempC                0
tempF                0
windspeedMiles       0
windspeedKmph        0
winddirDegree        0
winddir16Point       0
weatherCode          0
weatherIconUrl       0
weatherDesc          0
precipMM             0
precipInches         0
humidity             0
visibility           0
visibilityMiles      0
pressure             0
pressureInches       0
cloudcover           0
HeatIndexC           0
HeatIndexF           0
DewPointC            0
DewPointF            0
WindChillC           0
WindChillF           0
WindGustMiles        0
WindGustKmph         0
FeelsLikeC           0
FeelsLikeF           0
uvIndex              0
city                 0
dtype: int64

	date_time	sunrise	sunset	moonrise	moonset	moon_phase	moon_illumination	time	tempC	tempF	...	DewPointC	DewPointF	WindChillC	WindChillF	WindGustMiles	WindGustKmph	FeelsLikeC	FeelsLikeF	uvIndex	city
0	2016-10-31 00:00:00	06:40 AM	04:50 PM	07:02 AM	05:38 PM	New Moon	0	0	5	40	...	2	36	1	34	16	25	1	34	3	Bakar
1	2016-10-31 00:00:00	06:42 AM	04:54 PM	07:05 AM	05:41 PM	New Moon	0	0	14	58	...	8	47	12	54	22	35	12	54	4	Bale
2	2016-10-31 00:00:00	06:38 AM	04:50 PM	07:00 AM	05:38 PM	New Moon	0	0	9	48	...	5	41	6	43	17	28	6	43	3	Banjol
3	2016-10-31 00:00:00	06:26 AM	04:44 PM	06:48 AM	05:31 PM	New Moon	0	0	14	57	...	5	41	12	53	20	33	12	53	4	Baska
4	2016-10-31 00:00:00	06:41 AM	04:52 PM	07:04 AM	05:39 PM	New Moon	0	0	5	40	...	2	36	1	34	16	25	1	34	3	Boljun

5 rows × 37 columns

Correlation

Correlation file exists!

Correlation Map

Min comumunities: 4 -- Iter: 167 -- Resoluton value: 1.0166999999999982

Min comumunities: 5 -- Iter: 272 -- Resoluton value: 1.027199999999997

Min comumunities: 6 -- Iter: 318 -- Resoluton value: 1.0317999999999965

Export to HTML

[NbConvertApp] Converting notebook correlation.ipynb to HTML
[NbConvertApp] Writing 4538292 bytes to output/tempC_correlation.html
[NbConvertApp] Converting notebook correlation.ipynb to pdf
/home/HDD/00-Development/016-Diplomski/venv/lib/python3.9/site-packages/nbconvert/filters/datatypefilter.py:41: UserWarning: Your element with mimetype(s) dict_keys(['text/html']) is not able to be represented.
  warn(
[NbConvertApp] Support files will be in tempC_correlation_files/
[NbConvertApp] Making directory ./tempC_correlation_files
[NbConvertApp] Making directory ./tempC_correlation_files
[NbConvertApp] Making directory ./tempC_correlation_files
[NbConvertApp] Making directory ./tempC_correlation_files
[NbConvertApp] Making directory ./tempC_correlation_files
[NbConvertApp] Writing 68352 bytes to notebook.tex
[NbConvertApp] Building PDF
[NbConvertApp] Running xelatex 3 times: ['xelatex', 'notebook.tex', '-quiet']
[NbConvertApp] Running bibtex 1 time: ['bibtex', 'notebook']
[NbConvertApp] WARNING | bibtex had problems, most likely because there were no citations
[NbConvertApp] PDF successfully created
[NbConvertApp] Writing 4257294 bytes to output/tempC_correlation.pdf